Redes bayesianas para eleição da ventilação mecânica no pós-operatório de cirurgia cardíaca

Schenekenberg, Carolina Napoli Macedo; Malucelli, Andreia; Dias, João da Silva; Cubas, Márcia Regina

doi:10.1590/s0103-51502011000300013

Cited by 3 publications

(3 citation statements)

References 14 publications

(19 reference statements)

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“…The stages of selection and survey of the variables included in the descriptive typology. The BI planning and construction phase considered applied (Schenekenberg et al, 2011).…”

Section: 𝑃𝐺 = (4)mentioning

confidence: 99%

Scratchs Analysis of an LCC Project Using a Bayesian Network Model

Aparecido da Silva,

Borba Costa,

Costa Dantas

et al. 2023

IJBM

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Emphasizing the need to use models capable of dealing with the uncertainty of the project environment, especially those that can alleviate any scratchs that may arise during the course of the project, the following question arises: How to assess the scratchs of a project developed in LCC at from measurements from a Bayesian Network model? In this context, the goal of the study is to measure and analyze the scratchs of a project developed by the LCC method from a model elaborated in Bayesian Networks. The RB structure is composed of two parts, one with a qualitative approach, the other with a quantitative approach. From the information collected with a specialist, we sought to understand the strength that each item present in the key factors Requirements, Restrictions, Time Deliveries and Assumptions exerts on the key factor scratchs, in addition to the strength that each item of the key factor scratchs exerts together with the General Project Performance Indicator (IGDP). Based on the generated simulations, it seen which scratchs affected by a larger number of parameters in the project, as well as the scratchs that present a high or moderate probability of inferior performance with the IGDP. The research developed a probabilistic model that supplied the quantification of possible scratchs in the project, based on the belief of an expert. The developed RB allowed project managers to measure, and so deliberate on practical solutions to problems that affected by project scratchs, based on parameterized analyses.

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“…The stages of selection and survey of the variables included in the descriptive typology. The BI planning and construction phase considered applied (Schenekenberg et al, 2011).…”

Section: 𝑃𝐺 = (4)mentioning

confidence: 99%

Scratchs Analysis of an LCC Project Using a Bayesian Network Model

Aparecido da Silva,

Borba Costa,

Costa Dantas

et al. 2023

IJBM

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“…Thereby, the BN determines a single probability distribution for each variable, given by Equation 1, where π i is the set of variables, in which X i is conditionally dependent (Aneziris et al, 2010;Friedman et al, 1997;Leu & Chang, 2013;Pearl, 1988;Schenekenberg et al, 2011).…”

Section: Stage Ii: the Construction Of The Bnsmentioning

confidence: 99%

“…Such reduction provides benefits on inference, learning (parameter estimation) and on computational perspective, and the resulting model is more robust to determine the effects, the trend and the variance (Aneziris et al, 2010;Friedman et al, 1997;Leu & Chang, 2013;Pearl, 1988;Schenekenberg et al, 2011). With the structure of the BN set, and after the estimation of network parameters, one can then obtain the posterior probability distribution, and based on these data, infer about the occupational risk (Delcroix et al, 2013;Lee et al, 2013).…”

Section: Stage Ii: the Construction Of The Bnsmentioning

confidence: 99%

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Vieira

Silva

2017

Prod.

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A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

Collazo¹,

Pessôa²,

Bahiense

et al. 2016

Pesqui. Oper.

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ABSTRACT. Despite medical advances, mortality due to acute coronary syndrome remains high. For this reason, it is important to identify the most critical factors for predicting the risk of death in patients hospitalized with this disease. To improve medical decisions, it is also helpful to construct models that enable us to represent how the main driving factors relate to patient outcomes. In this study, we compare the capability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models to distinguish between patients hospitalized with acute coronary syndrome who have low or high risk of death. Input variables are selected using the wrapper approach associated with a mutual information filter and two new proposed filters based on Euclidean distance. Because of missing data, the use of a filter is an important step in increasing the size of the usable data set and maximizing the performance of the classification models. The computational results indicate that the SVM model performs better. The most relevant input variables are age, any previous revascularization, and creatinine, regardless of the classification algorithms and filters used. However, the Euclidean filters also identify a second important group of input variables: age, creatinine and systemic arterial hypertension.

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Redes bayesianas para eleição da ventilação mecânica no pós-operatório de cirurgia cardíaca

Cited by 3 publications

References 14 publications

Scratchs Analysis of an LCC Project Using a Bayesian Network Model

Scratchs Analysis of an LCC Project Using a Bayesian Network Model

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

A Comparative Study Between Artificial Neural Network and Support Vector Machine for Acute Coronary Syndrome Prognosis

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